Irfanuruchi/Qwen3-4B-Computer-Science-GGUF overview
Qwen3 4B Computer Science GGUF GGUF release of Qwen3 4B Computer Science , a specialized language model fine tuned for computer science, software engineering, …
Runs locally from ~2.33 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| Qwen3-4B-Computer-Science-BF16.gguf | GGUF | BF16 | 7.50 GB | Download |
| Qwen3-4B-Computer-Science-Q4_K_M.gguf | GGUF | Q4_K_M | 2.33 GB | Download |
| Qwen3-4B-Computer-Science-Q5_K_M.gguf | GGUF | Q5_K_M | 2.69 GB | Download |
| Qwen3-4B-Computer-Science-Q6_K.gguf | GGUF | Q6_K | 3.08 GB | Download |
| Qwen3-4B-Computer-Science-Q8_0.gguf | GGUF | Q8_0 | 3.99 GB | Download |
Model Details
| Model ID | Irfanuruchi/Qwen3-4B-Computer-Science-GGUF |
|---|---|
| Author | Irfanuruchi |
| Pipeline | text-generation |
| License | apache-2.0 |
| Base model | Qwen/Qwen3-4B |
| Last modified | 2026-07-24T10:58:31.000Z |
Model README
---
license: apache-2.0
library_name: gguf
tags:
- gguf
- llama.cpp
- text-generation
- conversational
- code
- programming
- computer-science
- qwen3
base_model: Qwen/Qwen3-4B
---
Qwen3-4B-Computer-Science-GGUF
GGUF release of Qwen3-4B-Computer-Science, a specialized language model fine-tuned for computer science, software engineering, Python programming, debugging, code generation, and technical reasoning.
This repository provides multiple GGUF quantizations for use with llama.cpp, LM Studio, Ollama, Jan, KoboldCpp, and other GGUF-compatible inference engines.
---
Model Overview
- Base Model: Qwen/Qwen3-4B
- Architecture: Qwen3
- Format: GGUF
- License: Apache-2.0
- Language: English
- Domain: Computer Science & Software Engineering
The model is designed to provide strong performance across software engineering tasks while remaining efficient enough to run locally on modern CPUs and GPUs.
---
Training Data
This model was fine-tuned using openly licensed datasets:
| Dataset | License |
|---------|---------|
| HuggingFaceTB/smoltalk (smol-magpie-ultra) | Apache-2.0 |
| agentica-org/DeepCoder-Preview-Dataset (primeintellect) | MIT |
Training split:
- 60,989 samples
Evaluation split:
- 512 samples
---
Available Quantizations
| File | Recommended Use |
|------|-----------------|
| BF16.gguf | Highest quality, requires significant memory |
| Q8_0.gguf | Near-BF16 quality |
| Q6_K.gguf | Excellent quality/performance balance |
| Q5_K_M.gguf | Recommended for most users |
| Q4_K_M.gguf | Best memory efficiency |
---
Recommended Quantization
For most systems:
Q5_K_M offers the best balance between:
- Quality
- Memory usage
- Speed
If memory is limited, use Q4_K_M.
If maximum quality is desired, use Q8_0 or BF16.
---
Example (llama.cpp)
./llama-cli \
-m Qwen3-4B-Computer-Science-Q5_K_M.gguf \
-c 8192
---
Example (LM Studio)
- Download one of the GGUF files.
- Import the model into LM Studio.
- Select the model.
- Start chatting.
---
Example (Ollama)
Create a Modelfile:
FROM Qwen3-4B-Computer-Science-Q5_K_M.gguf
Then run:
ollama create qwen3-cs -f Modelfile
ollama run qwen3-cs
---
Intended Use
This model is intended for:
- Software Engineering
- Python Programming
- Debugging
- Code Review
- Code Generation
- Technical Question Answering
- Algorithm Design
- Computer Science Education
---
Limitations
This model is specialized for computer science tasks.
Performance outside software engineering domains may differ from the original base model.
As with all language models:
- outputs may contain mistakes
- generated code should be reviewed
- security-critical code should always be validated
---
Integrity
SHA-256 hashes for every GGUF file are included in:
SHA256SUMS
Users are encouraged to verify downloaded files before use.
---
License
This repository is released under the Apache-2.0 License.
The fine-tuning datasets are compatible with commercial use:
- Apache-2.0
- MIT
---
Acknowledgements
- Alibaba Qwen Team
- Hugging Face
- HuggingFaceTB
- Agentica
- llama.cpp contributors
---
Citation
If you use this model in your work, please cite this repository.
@misc{qwen3_4b_computer_science_gguf,
title={Qwen3-4B-Computer-Science-GGUF},
author={Irfanuruchi},
year={2026},
publisher={Hugging Face}
}Run Irfanuruchi/Qwen3-4B-Computer-Science-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models